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IFCM:Fuzzy clustering for rule extraction of interval Type-2 fuzzy logic system

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2 Author(s)
Wei-bin Zhang ; Xian JiaoTong Univ., Xian ; Wen-jiang Liu

Compared with the traditional type-1 fuzzy logic system, type-2 fuzzy logic systems (T2FLS) are suitable to handle the situations where a great deal of uncertainty are present. However, how to extract fuzzy rules automatically from input/output data is still an important issue because sometimes human experts can not get valid rules from unknown systems. Fuzzy c-means clustering (FCM) is one of algorithms used frequently to extract rules from type-1 fuzzy logic system, but its application is merely limited to dots set. This paper introduces an enhanced clustering algorithm, called the interval fuzzy c-means clustering (IFCM), which is adequate to deal with interval sets. Moreover, it is shown that the proposed IFCM algorithm can be used to extract fuzzy rules from interval type-2 fuzzy logic system. Simulation results are included in the end to show the validity of IFCM.

Published in:

Decision and Control, 2007 46th IEEE Conference on

Date of Conference:

12-14 Dec. 2007